Collective intelligence

Estudio e implantación de algoritmos de recomendación, búsqueda, ranking y aprendizaje.

Inteligencia artificial en vehiculo

Desarrollo de un robot seguidor de líneas, en el que se implementan diversas soluciones de las áreas de sistemas embebidos e inteligencia artificial.

Estudi i implementació d'un sistema multiagent intel·ligent i la seva aplicació a sistemes difusos

Estudi i implementació d'un sistema multiagent intel·ligent i la seva aplicació a sistemes difusos. Utilització de les llibreries JADE i JFuzzyLogic.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Mejora diagnóstica de hepatopatías de afectación difusa mediante técnicas de inteligencia artificial

The automatic diagnostic discrimination is an application of artificial intelligence techniques that can solve clinical cases based on imaging. Diffuse liver diseases are diseases of wide prominence in the population and insidious course, yet early in its progression. Early and effective diagnosis is necessary because many of these diseases progress to cirrhosis and liver cancer. The usual technique of choice for accurate diagnosis is liver biopsy, an invasive and not without incompatibilities one. It is proposed in this project an alternative non-invasive and free of contraindications method based on liver ultrasonography. The images are digitized and then analyzed using statistical techniques and analysis of texture. The results are validated from the pathology report. Finally, we apply artificial intelligence techniques as Fuzzy k-Means or Support Vector Machines and compare its significance to the analysis Statistics and the report of the clinician. The results show that this technique is significantly valid and a promising alternative as a noninvasive diagnostic chronic liver disease from diffuse involvement. Artificial Intelligence classifying techniques significantly improve the diagnosing discrimination compared to other statistics.

Sistema d'anàlisi financer: tresoreria amb eines Business Intelligence

El projecte tracte d' implementar una solució de Business Intelligence sota la plataforma Microsoft.Aquest projecte va destinat al Departament de Comptabilitat de l' Ajuntament de Cambrils, i està relacionat amb la funció del control de les despeses i els ingressos

A Grid Supercomputing Enviroment for High Demand Computational Applications

Despite the huge increase in processor and interprocessor network performace, many computational problems remain unsolved due to lack of some critical resources such as floating point sustained performance, memory bandwidth, etc... Examples of these problems are found in areas of climate research, biology, astrophysics, high energy physics (montecarlo simulations) and artificial intelligence, among others. For some of these problems, computing resources of a single supercomputing facility can be 1 or 2 orders of magnitude apart from the resources needed to solve some them. Supercomputer centers have to face an increasing demand on processing performance, with the direct consequence of an increasing number of processors and systems, resulting in a more difficult administration of HPC resources and the need for more physical space, higher electrical power consumption and improved air conditioning, among other problems. Some of the previous problems can´t be easily solved, so grid computing, intended as a technology enabling the addition and consolidation of computing power, can help in solving large scale supercomputing problems. In this document, we describe how 2 supercomputing facilities in Spain joined their resources to solve a problem of this kind. The objectives of this experience were, among others, to demonstrate that such a cooperation can enable the solution of bigger dimension problems and to measure the efficiency that could be achieved. In this document we show some preliminary results of this experience and to what extend these objectives were achieved.

Programación de un robot autónomo: diseño, construcción, programación e integración de un robot autónomo en el entorno Pyro

En aquest projecte, s'ha dissenyat, construït i programat un robot autònom, dotat de sistema de locomoció i sensors que li permeten navegar sense impactar en un entorn controlat. Per assolir aquests objectius s'ha dissenyat i programat una unitat de control que gestiona el hardware de baix volum de dades amb diferents modes d'operació, abstraient-lo en una única interfície. Posteriorment s'ha integrat aquest sistema en l'entorn de robòtica Pyro. Aquest entorn permet usar i adaptar, segons es necessiti, eines d'intel·ligència artificial ja desenvolupades.

Intel·ligència artificial aplicada a un joc beat'em up en 2D

Creació d'un joc del tipus Arcade beat'em up en 2D utilitzant escenaris amb una certa profunditat de moviment i dotant als personatges no jugadors i altres objectes d'Intel·ligència Artificial de manera que el seu comportament no sigui sempre lineal i aprofitant-ho per afegir nivells de dificultat.

Diseño y Desarrollo de un cliente Web Processing Services (WPS) para gvSIG

Configuració d'un entorn de desenvolupament en el IDE Eclipse. Introducció als SIG. Usos, utilitats i exemples. Conèixer la eina gvSIG. Conèixer els estàndards més estesos de l'Open Geospatial Consortium (OGC) i en especial del Web Processing Services. Analitzar, dissenyar i desenvolupar un client capaç de consumir serveis wps.

Diseño y Desarrollo de un cliente Web Processing Service (WPS) para gvSIG

Estudi dels estàndards definits per l'Open Geospatial Consortium, i més concretament en l'estàndard Web Processing Service (wps). Així mateix, ha tingut una component pràctica que ha consistit en el disseny i desenvolupament d'un client capaç de consumir serveis Web creats segons wps i integrat a la plataforma gvSIG.

VLSI architecture for an Underwater Robot Vision System

It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great number of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. For normalised correlation criteria, previous experiments shown that the result is not altered in presence of nonuniform illumination. Usually, hardware for motion estimation has been limited to simple correlation criteria. The main goal of this paper is to propose a VLSI architecture for motion estimation using a matching criteria more complex than Sum of Absolute Differences (SAD) criteria. Today hardware devices provide many facilities for the integration of more and more complex designs as well as the possibility to easily communicate with general purpose processors

High-performance payload data handling system for Gaia

Gaia is the most ambitious space astrometry mission currently envisaged and is a technological challenge in all its aspects. We describe a proposal for the payload data handling system of Gaia, as an example of a high-performance, real-time, concurrent, and pipelined data system. This proposal includes the front-end systems for the instrumentation, the data acquisition and management modules, the star data processing modules, and the payload data handling unit. We also review other payload and service module elements and we illustrate a data flux proposal.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.